Best Practices for Operationalizing AI at Your Enterprise
Whenever data scientists and AI developers think about the real-world applications of their machine learning models, they don’t use the term “deployment”. Instead, the correct nomenclature would be to operationalize.
While sounding confusing for traditional IT application developers and operation managers, the idea is that the approach behind the AI operationalization of a MLOps platform , for example, is different from traditional software deployment. Let’s see below a detailed explanation behind this concept.
One of the main differences between an AI project in comparison with another software application development is that it does not follow the standard building, testing, deployment and management steps of operation. Instead, there are only two main phases of operation, which are the training and the inference phases.
The former involves the selection of one or multiple machine learning algorithms, the selection and identification of a clean and appropriate database, then the application of this data to the selected algorithm that is parametrized to result in an ML model, then finally the testing and validation of the model to ensure it is able to properly generalize other information without any issues.
The latter inference phase will then focus on the application of the created machine learning model to a specific use case, then the constant evaluation to safeguard the real-world data proper generalization of the model, then making any necessary adjustments or parametric configurations to continuously improve the machine learning artificial intelligence model. This phase can also determine if there are other possible use cases for the created model that can be broader than its original purpose.
To sum up, the training phase is usually happening in a controlled lab environment, while the inference phase will be an ongoing process during its real-life application. Certainly, some problems can arise from that. In the real world, there are multiple different platforms for machine learning, without any universal standard for that or AI per se, since there is a wide array of options when choosing which models to follow for classifying, predicting values and problem solution approaches. Machine learning models can be found from offline mobile applications to Internet-of-things devices or even in autonomous vehicles. Therefore, anywhere cognitive technology can find its use, you will be able to set up a machine learning model.
But what are the requirements for properly operationalizing AI? Well, for starters, any ML model should first pass its laboratory training phase to start inferring into a real-world application so it can prove its value and reliability other than pure scientific research. Proper AI systems should be completely owned and managed by the line of business that is responsible for the problem the machine learning model is trying to solve. Without that, there is no other way to accurately assess and manage its results in order to improve the artificial intelligence and set new parameters to correct it.
Companies that adopt this cognitive technology need to know that they have a probabilistic nature, which means it will not always produce a 100% accurate result at all times. This should be taken into consideration when operationalizing any AI system.